1. Field of the Invention
The present invention relates generally to a method for locating clusters on a two-dimensional scatter plot by automatically defining a position of at least one variable position, geometric boundary surface on the scatter plot so as to enclose a group of the displayed particles in a data cluster. The boundary surface has a polygonal shape defined by a plurality of vertices about at least one cell cluster. The present invention further relates to generating a cluster using a two-dimensional density estimate whereby the data is binned in a histogram and the bin counts subjected to smoothing. Bins are then assigned to respective clusters whereby clusters are separated by valleys in the density estimate.
2. Description of the Related Art
Flow cytometry, the measurement and/or separation of objects such as cells, nuclei, chromosomes and other particles in a moving liquid stream (“objects”), is well established as a valuable analysis tool in research and clinical laboratories. A discussion of the various principles, techniques and apparatus behind flow cytometry is set forth in an article by John L. Haynes, entitled “Principles of Flow Cytometry”, Cytometry Supplement 3:7-17 (1988), the disclosure of which is hereby incorporated by reference. Conventional flow cytometry devices for analyzing objects with specific characteristics basically consist of a liquid stream forming a sheath to focus the objects as they pass through an orifice associated with the analyzing or counting capabilities of the device. Usually, this type of analysis includes labeling the objects with one or more markers and then examining them for the presence or absence of one or more such markers. In the case of a cell, such as a leukocyte, tumor cell or microorganism, the marker can be directed to a molecule on the cell surface or to a molecule in the cytoplasm. Examination of a cell's physical characteristics, as well as the presence or absence of particular markers can be used to identify the population to which a cell belongs. Accordingly, there has been considerable interest in flow cytometry to analyze and sort objects. The objects can be analyzed and/or sorted at high speeds to collect tens of thousand of the objects based on a variety of chemical and physical characteristics such as size, granulation of the cytoplasm and presentation of specific antigens.
Flow cytometry comprises a well known methodology using multi-parameter data for identifying and distinguishing between different cell types in a sample. For example, the sample may be drawn from a variety of biological fluids, such as blood, lymph or urine, or may be derived from suspensions of cells from hard tissues such as colon, lung, breast, kidney or liver. In a flow cytometer, cells are passed, in suspension, substantially one at a time through one or more sensing regions where in each region each cell is illuminated by an energy source. The energy source generally comprises an illumination means that emits light of a single wavelength such as that provided by a laser (e.g., He/Ne or argon) or a mercury arc lamp with appropriate filters. Light at 488 nm is a generally used wavelength of emission in a flow cytometer having a single sensing region.
In series with a sensing region, multiple light collection means, such as photomultiplier tubes (or “PMTs”), are used to record light that passes through each cell (generally referred to as forward light scatter), light that is reflected orthogonal to the direction of the flow of the cells through the sensing region (generally referred to as orthogonal or side light scatter) and fluorescent light emitted form the cell, if it is labeled with a fluorescent marker(s), as the cell passes through the sensing region and is illuminated by the energy source. Each of forward light scatter (or “FSC”), orthogonal or side light scatter (or “SSC”), and fluorescence emissions (or “FL1,” “FL2,” etc.) comprise a separate parameter for each cell (or each “event”). Thus, for example, two, three, four or more parameters can be collected (and recorded) from a cell labeled with two different fluorescence markers. Flow cytometers further comprise data acquisition, analysis and recording means, such as a computer, wherein multiple data channels record data from each PMT for the light scatter and fluorescence emitted by each cell as it passes through the sensing region. The purpose of the analysis system is to classify and count cells wherein each cell presents itself as a set of digitized parameter values. Typically, by current analysis methods, the data collected in real time (or recorded for later analysis) is plotted in 2-D space for ease of visualization.
Such plots are referred to as “scatter plots” or “dot plots” and a typical example of a dot plot drawn from light scatter data recorded for leukocytes is shown in FIG. 1 of U.S. Pat. No. 4,987,086, the disclosure of which is hereby incorporated by reference in its entirety. By plotting orthogonal light scatter versus forward light scatter, one can distinguish between granulocytes, monocytes and lymphocytes in a population of leukocytes isolated from whole blood. By electronically (or manually) “gating” on only lymphocytes using light scatter, for example, and by the use of the appropriate monoclonal antibodies labeled with fluorochromes of different emission wavelength, one can further distinguish between cell types within the lymphocyte population (e.g., between T helper cells and T cytotoxic cells). U.S. Pat. Nos. 4,727,020, 4,704,891, 4,599,307, 4,987,086 and 6,014,904 describe the arrangement of the various components that comprise a flow cytometer, the general principles of use and one approach to gating on cells in order to discriminate between populations of cells in a blood sample. The disclosures of these patents are hereby incorporated by reference in their entireties.
Of particular interest is the analysis of cells from patients infected with HIV, the virus which causes AIDS. It is well known that CD4+ lymphocytes play an important role in HIV infection and AIDS. For example, counting the number of CD4+ T lymphocytes in a sample of blood from an infected individual will provide an indication of the progress of the disease. A cell count under 200 per cubic millimeter is an indication that the patient has progressed from being seropositive to AIDS. In addition to counting CD4+ T lymphocytes, CD8+ T lymphocytes also have been counted and a ratio of CD4:CD8 cells has been used in understanding AIDS.
In both cases, a sample of whole blood is obtained from a patient. Monoclonal antibodies against CD3 (a pan-T lymphocyte marker), CD4 and CD8 are labeled directly or indirectly with fluorescent dye. These dyes have emission spectra that are distinguishable from each other. Examples of such dyes are set forth in Example 1 of U.S. Pat. No. 4,745,285, the disclosure of which is hereby incorporated by reference in its entirety. The labeled cells then are run on the flow cytometer and data is recorded. Analysis of the data can proceed in real time or be stored in list mode for later analysis.
While data analyzed in 2-D space can yield discrete populations of cells, most often the dot plots represent projections of multiple clusters. As a result, often it is difficult to distinguish between cells which fall into regions of apparent overlap between clusters. In such cases, cells can be inadvertently classified in a wrong cluster, and thus, contribute inaccuracy to the population counts and percentages being reported. In blood from an HIV infected patient for example, over-inclusion of T cells as being CD4+ could lead a clinician to believe a patient had not progressed to AIDS, and thus, certain treatment which otherwise might be given could be withheld. In cancers, such as leukemia, certain residual tumor cells might remain in the bone marrow after therapy. These residual cells are present in very low frequencies (i.e., their presence is rare and thus their occurrence in a large sample is a “rare event”), and thus, their detection and classification are both difficult and important.
One known method for solving this problem relies on a gravitational attractor consisting of a geometric boundary surface of fixed size, shape and orientation, but of variable position, and a computational engine by which the boundary surface positions itself optimally to enclose a cluster of multi-parameter events, with multiple attractors for simultaneously classifying multiple clusters of events within the same datastream or recorded data distribution. The strategy is to assign one attractor per population to be identified and/or sorted. This method is described in U.S. Pat. No. 5,627,040, the disclosure of which is hereby incorporated by reference in its entirety. However, there are some limitations to this method. For example, because of the fixed size, shape and orientation of the boundary surface, some cells can be inadvertently classified in a wrong cluster or omitted from inclusion within the boundary, and therefore contribute inaccuracy to the population counts and percentages being reported. Thus, there has been a need for a method for more accurately discriminating between clusters of cells, and therefore for more accurately identifying and/or sorting cells into different populations.